Comparison of Two‐Dimensional and Three‐Dimensional Macroscopic Invasion Percolation Simulations with Laboratory Experiments of Gas Bubble Flow in Homogeneous Sands
Bibliographic record
Abstract
The upward flow of gas plays a role in many subsurface systems, including those related to oil and gas recovery, carbon dioxide storage, and groundwater remediation. Macroscopic invasion percolation (macro‐IP) is a modeling approach suitable for the simulation of upward gas flow, including bubble flow, in porous media, but few studies have compared simulations with experiments. Monte Carlo suites of macro‐IP simulations in two‐ and three‐dimensional domains were compared with small‐scale (∼10 cm) thin‐tank experiments of gas injection in homogeneous, initially water‐saturated sand, where transient gas saturations were quantified at the local scale. Comparisons were based on gas saturations and the spatial moments of the gas distribution and were performed for resolutions between 1 by 1 mm and 5 by 5 mm. Simulations were conducted both with and without a stochastic selection modification of the macro‐IP approach. Two‐dimensional simulations without stochastic selection were able to reproduce the spatial moments of the experimental gas distributions using reasonable estimates of local gas saturations at resolutions coarser than or equal to 2 by 2 mm. Three‐dimensional simulations were also able to reproduce the spatial moments at a resolution of 4 by 4 mm, but required higher‐than‐expected gas saturations to accurately represent the injected gas volume. Finer discretizations in two‐ and three‐dimensional simulations were unable to reproduce injected gas volumes without considering stochastic selection or without the use of unreasonably high local gas saturations. This suggests a lower limit on the grid block size for macro‐IP without stochastic selection of approximately three to four grain diameters. By including stochastic selection of the next invaded site in the macro‐IP simulations, observed gas saturations could be reproduced using finer discretization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".